analyze

分类: 数据与AI | 上传者: H-mmer | 下载: 0 | 版本: v1.0（最新）

使用人工智能分析侦察输出，以建议高价值目标和攻击策略。用法：/analyze <目标>

更新日志: Source: GitHub https://github.com/H-mmer/pentest-agents

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📄 SKILL.md 1.8 KB

SKILL.md

---
name: analyze
description: "Analyze recon output with AI to suggest high-value targets and attack strategies. Usage: /analyze <target>"
disable-model-invocation: true
---
AI-powered analysis of recon data for: $ARGUMENTS

## Process
1. Read all recon data: `ls recon/` and read key files
2. Read brain data: `uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py brief $ARGUMENTS`
3. Read tech stack intel: `uv run python3 $CLAUDE_PROJECT_DIR/tools/intel_engine.py suggest <detected-stack>`
4. Read hacktivity patterns: `uv run python3 $CLAUDE_PROJECT_DIR/tools/intel_engine.py analyze`

## Analysis Tasks (do all of these)

### Crown Jewel Mapping
What's the most valuable thing an attacker could access on this target?
- Financial data? → hunt IDOR on payment/billing endpoints
- User PII? → hunt IDOR on profile/export endpoints
- Admin access? → hunt auth bypass on admin endpoints
- Infrastructure? → hunt SSRF → cloud metadata

### Attack Path Ranking
Given the tech stack and recon output, rank the top 5 attack paths by:
1. Likelihood of vulnerability existing (based on tech stack patterns)
2. Impact if exploited (based on endpoint function)
3. Competition (based on hacktivity — avoid heavily-reported vuln classes)
4. Your past success (from brain patterns)

### Blind Spot Detection
What has NOT been tested? What endpoints have no brain data?
Cross-reference recon output against brain tested endpoints.
Flag untested high-value endpoints.

### Output
```
ANALYSIS: target.com
═════════════════════

Crown Jewels: [what's most valuable]

Top 5 Attack Paths:
1. [endpoint] × [vuln class] — likelihood: HIGH, impact: CRITICAL
2. ...

Blind Spots (untested P1 surface):
- /api/v2/payments/* — NO DATA in brain
- /api/v2/admin/* — NO DATA in brain

Recommendation: /hunt target.com --vuln-class [best bet]
```

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analyze

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